Class 02

Load Packages

library(Hmisc)
library(GGally)
library(palmerpenguins)
library(tidyverse)

theme_set(theme_bw())

Welcome Back

Correlation

• Source: XKCD

What is data science?

Source: Figure 1.1 in https://r4ds.hadley.nz/intro.html

Chatfield’s Six Rules for Data Analysis

1. Do not attempt to analyze the data until you understand what is being measured and why.
2. Find out how the data were collected.
3. Look at the structure of the data.
4. Carefully examine the data in an exploratory way, before attempting a more sophisticated analysis.
5. Use your common sense at all times.
6. Report the results in a clear, self-explanatory way.

Chatfield, Chris (1996) Problem Solving: A Statistician’s Guide, 2nd ed.

Introduction to R

• R is an open source statistical programming language managed by a core team of 15 people and thousands of code writers/statisticians who contribute their work
• Most of R is written in R
• Community available for fixing bugs/software
• Promotes reproducible research through open and accessible tools

R vs RStudio

• R is the programming language itself
• RStudio is an interface for working with R

RStudio

RStudio Default Screen

Projects

Projects are a convenient way to keep all your files for an analysis in one place.

Go to File > New Project to begin one now. Call the project “homework1” and save it to your computer in a folder for this class.

Types of Document

• R script

  • End with .R and are pure code. If you run them, output will appear in the bottom left corner called the console.

• Quarto documents

  • End in .qmd and use markdown language to turn characters into formatted text.

  • Processes code in code chunks, and output appears directly in the document

• Begin a new markdown script now

Environment

Your project now has it’s own “environment” in which you can store your data, variables and results.

Add a code chunk to your document, copy the code below, and run it.

Example:

x <- c(1, 2, 3, 4, 5)

Environment (cont.)

Stored variable now appears in the environment

Packages

Packages are collections of functions to use for statistical analyses. Some are loaded automatically, and some need to be separately installed. Let’s install the tidyverse package.

Package Install & Update Panel

Install Packages

Either…

Install packages

or…

install.packages('tidyverse',
                 dependencies = T)
install.packages('openintro',
                 dependencies = T)

Getting Help

Search for functions, packages, vignettes, and more directly in RStudio in the “Help” panel.

Help Panel

Exercise: Palmer Penguins

The Palmer Penguins

Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218.

Install the package and load the library

library(palmerpenguins)
library(tidyverse)

Find the data

data(package = 'palmerpenguins')

# Add the data to your environment
penguins <- penguins

Inspect the data

head(penguins)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 2 more variables: sex <fct>, year <int>

Dataframes

• Data structure in rows and columns like a spreadsheet

• Rows: (ideally) uniquely identified observations

• Columns: parameters which describe the observations

How many rows does penguins have?

nrow(penguins)

[1] 344

How many variables does penguins have?

ncol(penguins)

[1] 8

colnames(penguins)

[1] "species"           "island"            "bill_length_mm"   
[4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
[7] "sex"               "year"             

Can I find this out more quickly?

glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

How else can I get a description of the data?

Use the Hmisc::describe() function to quickly summarize data.

Hmisc::describe(penguins)

How else can I get a description of the data?

penguins 

 8  Variables      344  Observations
--------------------------------------------------------------------------------
species 
       n  missing distinct 
     344        0        3 
                                        
Value         Adelie Chinstrap    Gentoo
Frequency        152        68       124
Proportion     0.442     0.198     0.360
--------------------------------------------------------------------------------
island 
       n  missing distinct 
     344        0        3 
                                        
Value         Biscoe     Dream Torgersen
Frequency        168       124        52
Proportion     0.488     0.360     0.151
--------------------------------------------------------------------------------
bill_length_mm 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
     342        2      164        1    43.92    6.274    35.70    36.60 
     .25      .50      .75      .90      .95 
   39.23    44.45    48.50    50.80    51.99 

lowest : 32.1 33.1 33.5 34   34.1, highest: 55.1 55.8 55.9 58   59.6
--------------------------------------------------------------------------------
bill_depth_mm 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
     342        2       80        1    17.15    2.267     13.9     14.3 
     .25      .50      .75      .90      .95 
    15.6     17.3     18.7     19.5     20.0 

lowest : 13.1 13.2 13.3 13.4 13.5, highest: 20.7 20.8 21.1 21.2 21.5
--------------------------------------------------------------------------------
flipper_length_mm 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
     342        2       55    0.999    200.9    16.03    181.0    185.0 
     .25      .50      .75      .90      .95 
   190.0    197.0    213.0    220.9    225.0 

lowest : 172 174 176 178 179, highest: 226 228 229 230 231
--------------------------------------------------------------------------------
body_mass_g 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
     342        2       94        1     4202    911.8     3150     3300 
     .25      .50      .75      .90      .95 
    3550     4050     4750     5400     5650 

lowest : 2700 2850 2900 2925 2975, highest: 5850 5950 6000 6050 6300
--------------------------------------------------------------------------------
sex 
       n  missing distinct 
     333       11        2 
                        
Value      female   male
Frequency     165    168
Proportion  0.495  0.505
--------------------------------------------------------------------------------
year 
       n  missing distinct     Info     Mean      Gmd 
     344        0        3    0.888     2008   0.8919 
                            
Value       2007  2008  2009
Frequency    110   114   120
Proportion 0.320 0.331 0.349

For the frequency table, variable is rounded to the nearest 0
--------------------------------------------------------------------------------

Meet the penguins!

Meet the Palmer Penguins

Types of data

The key distinction we’ll make is between

• quantitative (numerical) and
• categorical (qualitative) information.

Information that is quantitative describes a quantity.

• All quantitative variables have units of measurement.
• Quantitative variables are recorded in numbers, and we use them as numbers (for instance, taking a mean of the variable makes some sense.)

Continuous vs. Discrete Quantities

Continuous variables (can take any value in a range) vs. Discrete variables (limited set of potential values)

• Is time a continuous or a discrete variable?

Qualitative (Categorical) Data

Qualitative variables consist of names of categories.

• Each possible value is a code for a category (could use numerical or non-numerical codes.)
  • Binary categorical variables (two categories, often labeled 1 or 0)
  • Multi-categorical variables (three or more categories)
• Can distinguish nominal (no underlying order) vs. ordinal (categories are ordered.)

Some Categorical Variables

• How is your overall health?
  (Excellent, Very Good, Good, Fair, Poor)
• Which candidate would you vote for if the election were held today?
• Did this patient receive this procedure?
• If you needed to analyze a small data set right away, which of the following software tools would you be comfortable using to accomplish that task?

Are these quantitative or categorical?

1. Do you smoke? (1 = Non-, 2 = Former, 3 = Smoker)
2. How much did you pay for your most recent haircut? (in $)
3. What is your favorite color?
4. How many hours did you sleep last night?
5. Statistical thinking in your future career? (1 = Not at all important to 7 = Extremely important)

• If quantitative, are they discrete or continuous? Do they have a meaningful zero point?
• If categorical, how many categories? Nominal or ordinal?

Data Dictionary

How do you visualize variables?

• Histogram (bar plot)

• Density, violin plot

• Boxplot

Histogram

penguins |>
  ggplot(aes(x = bill_length_mm)) +
  geom_histogram(binwidth = 2,
                 fill = 'steelblue',
                 col = 'white') +
  labs(title = 'Distribution of Bill Lengths',
       subtitle = 'In Adelie, Chinstrap, and Gentoo Penguins',
       x = 'Bill Length (mm)',
       y = '# of Individuals')

Histogram

Density Plot

penguins |>
  ggplot(aes(x = bill_length_mm)) +
  geom_density(fill = 'steelblue', alpha = 0.5) +
  labs(title = 'Distribution of Bill Lengths',
       subtitle = 'In Adelie, Chinstrap, and Gentoo Penguins',
       x = 'Bill Length (mm)',
       y = '# of Individuals')

Density Plot

Boxplot + Violin

penguins |>
  ggplot(aes(x = species, y = bill_length_mm)) +
  geom_violin(aes(fill = species)) +
  geom_boxplot(width = 0.3)

Boxplot + Violin

How do you find relationships between variables?

• Develop a research question

• Examine summary statistics

• Data exploration

Visualizing the Data - Scatterplot Matrices

penguins |>
  select(species, island, bill_length_mm,
         bill_depth_mm) |>
  ggpairs()

Visualizing the Data - Scatterplot Matrices

Visualizing the Data - Scatterplot Matrices

penguins |>
  select(flipper_length_mm, body_mass_g,
         sex, year) |>
  ggpairs()

Visualizing the Data - Scatterplot Matrices

Plotting Relationships

penguins |>
  ggplot(aes(x = bill_depth_mm,
             y = bill_length_mm)) +
  geom_point()

Plotting Relationships

Adding Regression Lines - LM

penguins |>
  ggplot(aes(x = bill_depth_mm,
             y = bill_length_mm)) +
  geom_point() +
  geom_smooth(method = 'lm', col = 'red')

Adding Regression Lines - LM

Plotting Regression Lines - LOESS

penguins |>
  ggplot(aes(x = bill_depth_mm,
             y = bill_length_mm)) +
  geom_point() +
  geom_smooth(method = 'loess', col = 'blue')

Plotting Regression Lines - LOESS

Plotting Regression Lines - By Group

penguins |>
  ggplot(aes(x = bill_depth_mm,
             y = bill_length_mm,
             col = species, group = species)) +
  geom_point() +
  geom_smooth(method = 'lm')

Plotting Regression Lines - By Group

Quarto Resources

• How to use Quarto in RStudio: https://quarto.org/docs/get-started/hello/rstudio.html

• Markdown language basics: https://quarto.org/docs/authoring/markdown-basics.html

• Themes for projects: https://quarto.org/docs/output-formats/html-themes.html